RevRx™: Unlocking the Power of Media Mix Modeling

“What am I going to invest my marketing budget into?” An age-old question that’s always been challenging to answer. Now, it’s even harder to answer in healthcare due to privacy concerns and the removal of tracking pixels, diverse channel mixes, and the complexity of multi-touch attribution. This has many companies turning to Media Mix Modeling (MMM) to figure out how to most efficiently budget their next marketing dollar. Previously inaccessible to most companies, recent advances in AI have made MMM a feasible and attractive solution. Learn about why you’d want to harness the power of MMM and how Cardinal has done just that with our new business intelligence tool RevRx™.

 

What Is Media Mix Modeling?

Media Mix Modeling (MMM) is a statistical analysis technique used to determine the effectiveness of different marketing channels and their impact on revenue or other key performance metrics. By analyzing historical data, MMM helps marketers understand how each channel—such as paid search, social media, TV, or even print—contributes to overall performance. It allows for more informed budget allocation by revealing which channels deliver the highest return on investment, helping organizations optimize their marketing spend. With recent advancements in AI, MMM has become more accessible, providing actionable insights for making data-driven decisions in an increasingly complex marketing landscape.

 

Why Would I Want Media Mix Modeling?

Aside from the catchy alliteration, there are three main benefits to media mix modeling:

1) Optimizing Media Mix with Tactical Investment

MMM offers a holistic statistical look at the impact of various marketing investment decisions on overall revenue. It can help you understand how big an effect your marketing team has on revenue in the first place and then help you optimize your channel mix to maximize your budgeting efficiency to drive more revenue.

Because media mix modeling or investment mix modeling lets you see how each marginal investment might affect overall revenue, you get a much better sense of where to effectively spend that next dollar. Our Chief Strategy Officer, Rich Briddock, explains it like this:

“What that forces you to look at is the margins and things like the marginal cost per acquisition of a channel that you’re currently running. If you have been 100% search-focused, you may have a blended cost per acquisition of $50, and you may say, ‘I love that. I love that my blended acquisition cost is $50. That’s within my target,’ but your least efficient cost per acquisition may be $1,000. The next dollar you invest may be $1,000, right? That might be way above your target, and you’re actually losing money on any additional dollar that you’re putting into search.”

By making good use of MMM, you can avoid the pitfalls of throwing money at the wrong channel based purely on average cost metrics and recognize the opportunity to invest in something like social or display that will give you a better return on your investment.

2) Longer-Term Strategic Planning

By making use of historical data to construct a statistical model that can be mined for business insights, MMM lends itself to various sorts of long-term strategic planning, such as:

• Annual planning: based on predictive forecasting to maximize marketing efficacy.
• Campaign strategy: designed for the channels you know will be most effective.
• Multi-channel strategy: based on how channels affect *total* revenue, not just channel revenue.

3) Communicating Budget Recommendations with the C-suite

One of the toughest challenges for any marketing leader is finding the hard data to back up your budget recommendations. It can be difficult to justify expenditures to the C-suite that aren’t backed by a clear dollar attribution. But MMM can help show how that investment impacts total revenue, as our Chief Growth Officer, Lauren Leone, explains:

“Say you’re a marketing leader in your organization and you’ve really struggled to get out of the paid search universe because nothing else converts at the same rate or you can’t attribute at the same rate. Maybe your cost per acquisitions aren’t the same on Meta or Display, so your tendency is to continue to turn those channels off because the person giving you the dollars is saying, ‘This isn’t efficient!’ [MMM] is another mechanism through which we can help C-levels understand the impact of some of these upper funnel marketing activities, maybe more traditionally branding initiatives, and show how they are driving actual revenue for the business.”

 

Why Is Media Mix Modeling Gaining Popularity?

Well, all those reasons above certainly explain why healthcare marketers would be eager to make use of media mix modeling. But there are two other big reasons MMM has recently seen a big bump in interest: 

1) It’s suddenly within reach.

Advanced statistical modeling has always been popular with billion-dollar consumer goods companies looking to maximize their ROI, and the biggest healthcare conglomerates have made use of it as well. However, when media mix modeling was first introduced, it took hundreds of thousands of dollars to develop a model. Even when specialist agencies began licensing their AI models for wider use, the price tag was six figures a month, effectively making it unrealistic for all but the wealthiest healthcare companies. 

That’s no longer the case.

Technology has advanced, and with AI computing likewise advancing, media mix modeling solutions are now faster, cheaper, and more accessible to any company with a few thousand dollars a month to throw at cutting-edge data analysis. Back when specialist agencies began licensing their models for quarterly reports, the models just couldn’t keep up with the fast-paced world of marketing. But today, MMM scenarios can be run in as little as 15 minutes, giving you instant data to strategize around. 

For healthcare marketers looking for directional signals on where to invest and how to grow, MMM is now a very viable option.

2) There’s no longer a good alternative.

For a time, multi-touch attribution was the holy grail of healthcare marketing. But as you know, if you’ve actually looked into doing multi-touch attribution with a diverse media mix across TikTok, Meta, programmatic, etc., it’s a lot more difficult than it used to be in various ways:

• Many platforms have their own attribution models: making it hard to unify them into one attribution model
• View-through impression-based conversions: difficult to track in a universal model
• Cookie depreciation: makes tracking more difficult
• HIPAA restrictions: being in compliance means not using tracking pixels

Is it still possible to put together multi-touch attribution? Technically, yes. But all these additional hurdles mean that it’s going to require massive investments of money and expert hours, putting multi-touch attribution today where MMM used to be: out of reach for all but the wealthiest companies with dollars to burn.

 

What Inputs Do You Need For The MMM Model?

As a starting point, I’d recommend: 

• 24 months of lead and booked appointment data.
• 24 months of media investment and media front-end data by channel 
• 24 months of revenue data
• Key macro factors that did influence and may influence performance.
• Estimated SEO contributions.

The more data you have, the better. So, if your revenue data is broken down by week and not just annualized, obviously, that’s going to give you a much more accurate picture of seasonal changes and so forth. If your weekly investment data is broken down by tactic in addition to just channel, that’s more information for the model.

And don’t forget to account for any external factors that influence performance. For example, if you added a call center in August, the model isn’t going to account for that unless you include that information. The more data we can provide our models, the more accurately they’ll be able to make predictions.

 

Media Mix Modeling Can’t Do Everything

While MMM offers many exciting possibilities, some people get too excited about a new hi-tech option and presume it can immediately do the job of half their marketing team. It can’t. MMM is a tool for aiding in strategic planning, but MMM: 

• Can’t replace your healthcare marketing strategist
• Can’t replace your hands-on-keyboard media manager
• Can’t replace analytics and traditional reporting
• Can’t watch signals and performance for you to manage your media budget
• Can’t tell you what to do on a weekly basis

MMM can only make statistical inferences based on historical data, which means it also:

• Takes time to collect and analyze data
• Can’t provide immediate answers to your burning questions
• Can’t offer actionable insights on optimizing an individual marketing campaign
• Can’t give insight into a patient’s behavior
• Can’t measure the effectiveness of an individual marketing campaign

 

RevRx™: More Than Just an MMM

Our proprietary business intelligence tool, RevRx™, is built upon a complex MMM that uses a full-information Bayesian econometric model. But more importantly, it’s also built on Cardinal’s deep healthcare expertise derived from a decade and a half of media experience, which has informed our understanding of what inputs to feed the model. For this reason, we think of RevRx™ more like investment mix modeling than media mix modeling because we consider a lot more than just media when developing the most effective investment strategy.

Chief Growth Officer, Lauren Leone, explains:

“What are the outputs, and how should you be using them? We’ve got all these inputs. We’re then understanding the marginal impact of investing in any given channel, maybe even any given tactic. That doesn’t just make your investment inherently work better for you at that point. You have to then know what to do with it. When you’ve got multiple locations, multiple service lines, providers, and capacity data, you need to know how you use those inputs, plus what you know about the effective marginal ROI of your channels, to make the right choices.”

That’s what CMOs really need to identify optimal investment strategies by analyzing practice capacity, service line priorities, and growth goals. Not just to see what channel an MMM recommends as an optimized ROI, but to be able to add constraints based on things like your current capacity data so you’re investing in the most effective way for your actual circumstances rather than the most effective way “in theory” that doesn’t work in the real world where your high-performing location only has three providers.

 

What Can I Do With RevRx™?

RevRx™ provides statistically modeled forecasting based on historical data, allowing you to:

• Analyze Past Performance (and determine channel contribution)
• Measure Incremental Channel Value (for each channel towards conversion)
• Get Clear-Cut Budget Guidance (based on model predictions with <4% error)
• Scale Smarter  (despite growing complexity)
• Account For Nuance  (across systems, service lines, and markets)
• Spot Diminishing Returns  (in case it’s time to diversify)
• Show Data Supporting Your Investment Strategy (to show the C-suite)
• Meet and Exceed Goals (both executive and private equity)

 

Why RevRx™ Includes Human Analysis

We’re excited to harness the power of media mix modeling, but what really makes it work is the human expertise surrounding it. We know what to put into the model, and equally importantly, we know how to interpret MMM outputs to devise an effective and efficient strategy. 

As CSO Rich Briddock tells it:

“The other key piece with RevRx™ that we’re bringing to the investment mix modeling approach is somebody who is evaluating how mature the channels are in the mix, how far through that optimization curve they are. Essentially, what else can we get out of that channel over time as we continue to test and optimize? You can even put those things in the model. You can say, on paid social media, I’m getting a $50 cost per acquisition (CPA) right now, but we’ve only just started running it. Based on our experience, we expect to be able to test and optimize to a point where we can lower CPAs by 50%. You can tell the model that actually, three months from now, I’m expecting my acquisition costs on paid [social] through the optimization that we make at Cardinal to be half of what it is today, and to factor that in when you’re making projections and investment recommendations based on the mix.”

We’ve had clients come to us and say that they ran an MMM, saw the marginal ROI, and now plan to invest in certain channels. But we can audit those channels and notice that they’re not performing efficiently—and then advise our clients to first optimize their channels with best practices to boost efficiency and then re-run the model to calculate optimal investments.

 

Harness the Power of RevRx™

For years, our business intelligence capabilities—from incrementality testing to forecasting to regression modeling—have been a cornerstone of our strategic media planning. And we’re now making it easier for more healthcare companies to access these insights, pairing the power of media mix modeling with the guidance of experienced healthcare media strategists. That’s what we’re excited to share with RevRx™: a holistic view of the connection between marketing investments and your overall revenue goals.

RevRx™ will work best for healthcare brands that are spending at least $70k per month in paid media that can go into the investment model. It’s going to be less effective for brands spending less than that—or brands spending less than 1% of their revenue in marketing even if that’s over $70k—because the model relies on making correlations between marketing spend and revenue, so the volume needs to be there.

But especially for any hyperscale healthcare brands that have full-funnel media investments, if you’re sick of the guessing game of traditional media planning and want data to build a channel mix that lowers CAC while meeting your patient growth goals, RevRx™ was very much designed with you in mind, so we hope you’ll take a look: Learn More about RevRx™